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在植入的西瓜种植中对布里克斯的定量评估:基于机器学习和回归的方法

Uğur Ercan¹, Ilker Sonmez², Aylin Kabaş³

¹Department of Informatics, Akdeniz University, 07070 Antalya, Türkiye.

Foods (Basel, Switzerland)

|December 17, 2024

概括

支持向量回归 (SVR) 准确地预测了瓜子布里克斯含量,超过了多重线性回归 (MLR). 这种机器学习方法提高了瓜子的非破坏性质量评估,并优化了种植,以提高水果的甜度.

科学领域:

园艺和农学园艺和农学
机器学习在农业中的应用
植物生理学植物生理学

背景情况:

瓜子水果的质量受根茎选择和生物化学成分的影响.
准确评估水果的质量特征,如布里克斯含量,对于农业管理至关重要.
机器学习为非破坏性质量评估提供了潜力.

研究的目的:

为了比较支持向量回归 (SVR) 和多重线性回归 (MLR) 模型来预测瓜子的布里克斯含量.
分析不同根茎对瓜子水果生物化学性能的影响.
建立一种可靠的,非破坏性的方法来评估瓜子的质量.

主要方法:

接种不同根茎的瓜子植物 (神,阿尔巴特罗斯,迪内罗).
测量水果的生物化学参数,包括,,,,,,和Brix.
使用性能指标 (MAE,MAPE,MSE,RMSE,R2) 开发和评估SVR和MLR模型.

主要成果:

该SVR模型在预测布里克斯含量方面表现出卓越的准确性,达到0.9904的R2,明显优于MLR模型 (R2:0.9472).
在布里克斯水平和糖含量 (糖糖,葡萄糖,果糖) 之间观察到强烈的相关性,可定位酸度的影响最小.
与MLR相比,SVR被证明是比MLR更可靠和非破坏性的瓜子质量评估方法.

结论:

SVR是一种高效的机器学习工具,用于非破坏性的瓜子质量评估,特别是用于布里克斯含量预测.
根茎选择显著影响瓜子水果的生物化学,可以使用SVR建模.
这些发现支持优化根茎管理和种植实践,以提高瓜果的质量.

关键词:

布里克斯 (Brix) 是一个数字.这是一个SVRSVR.接种方式接种方式瓜子瓜子瓜子

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